diff --git a/src/Microsoft.ML.Data/Transforms/Normalizer.cs b/src/Microsoft.ML.Data/Transforms/Normalizer.cs
index e0f5d5f019..bf2c84c4c4 100644
--- a/src/Microsoft.ML.Data/Transforms/Normalizer.cs
+++ b/src/Microsoft.ML.Data/Transforms/Normalizer.cs
@@ -26,6 +26,43 @@
 
 namespace Microsoft.ML.Transforms
 {
+    /// <summary>
+    /// <see cref="IEstimator{TTransformer}"/> for the <see cref="NormalizingTransformer"/>.
+    /// </summary>
+    /// <remarks>
+    /// <format type="text/markdown"><![CDATA[
+    ///
+    /// ###  Estimator Characteristics
+    /// |  |  |
+    /// | -- | -- |
+    /// | Does this estimator need to look at the data to train its parameters? | Yes |
+    /// | Input column data type | <xref:System.Single> or <xref:System.Double> or a known-sized vector of those types. |
+    /// | Output column data type | The same data type as the input column |
+    ///
+    /// The resulting NormalizingEstimator will normalize the data in one of the following ways based upon how it was created:
+    /// * Min Max - A linear rescale that is based upon the minimum and maximum values for each row.
+    /// * Mean Variance - Rescale each row to unit variance and, optionally, zero mean.
+    /// * Log Mean Variance - Rescale each row to unit variance based on a log scale.
+    /// * Binning - Bucketizes the data in each row and performs a linear rescale based on the calculated bins.
+    /// * Supervised Binning - Bucketize the data in each row and performas a linear rescale based on the calculated bins. The bin calculation is based on correlation of the Label column.
+    ///
+    /// ### Estimator Details
+    /// The interval of the normalized data depends on whether fixZero is specified or not. fixZero defaults to true.
+    /// When fixZero is false, the normalized interval is $[0,1]$ and the distribution of the normalized values depends on the normalization mode. For example, with Min Max, the minimum
+    /// and maximum values are mapped to 0 and 1 respectively and remaining values fall in between.
+    /// When fixZero is set, the normalized interval is $[-1,1]$ with the distribution of the normalized values depending on the normalization mode, but the behavior is different.
+    /// With Min Max, the distribution depends on how far away the number is from 0, resulting in the number with the largest distance being mapped to 1 if its a positive number
+    /// or -1 if its a negative number. The distance from 0 will affect the distribution with a majority of numbers that are closer together normalizing towards 0.
+    ///
+    /// To create this estimator use one of the following:
+    /// * [NormalizeMinMax](xref:Microsoft.ML.NormalizationCatalog.NormalizeMinMax(Microsoft.ML.TransformsCatalog, System.String, System.String, System.Int64, System.Boolean))
+    /// * [NormalizeMeanVariance](xref:Microsoft.ML.NormalizationCatalog.NormalizeMeanVariance(Microsoft.ML.TransformsCatalog,System.String,System.String,System.Int64,System.Boolean,System.Boolean))
+    /// * [NormalizeLogMeanVariance](xref:Microsoft.ML.NormalizationCatalog.NormalizeLogMeanVariance(Microsoft.ML.TransformsCatalog,System.String,System.String,System.Int64,System.Boolean))
+    /// * [NormalizeBinning](xref:Microsoft.ML.NormalizationCatalog.NormalizeBinning(Microsoft.ML.TransformsCatalog,System.String,System.String,System.Int64,System.Boolean,System.Int32))
+    /// * [NormalizeSupervisedBinning](xref:Microsoft.ML.NormalizationCatalog.NormalizeSupervisedBinning(Microsoft.ML.TransformsCatalog,System.String,System.String,System.String,System.Int64,System.Boolean,System.Int32,System.Int32))
+    /// ]]>
+    /// </format>
+    /// </remarks>
     public sealed class NormalizingEstimator : IEstimator<NormalizingTransformer>
     {
         [BestFriend]
@@ -284,6 +321,9 @@ public SchemaShape GetOutputSchema(SchemaShape inputSchema)
         }
     }
 
+    /// <summary>
+    /// <see cref="ITransformer"/> resulting from fitting an <see cref="NormalizingEstimator"/>.
+    /// </summary>
     public sealed partial class NormalizingTransformer : OneToOneTransformerBase
     {
         internal const string LoaderSignature = "Normalizer";
diff --git a/src/Microsoft.ML.Transforms/NormalizerCatalog.cs b/src/Microsoft.ML.Transforms/NormalizerCatalog.cs
index 7453efbaac..ccf0f35bb7 100644
--- a/src/Microsoft.ML.Transforms/NormalizerCatalog.cs
+++ b/src/Microsoft.ML.Transforms/NormalizerCatalog.cs
@@ -31,11 +31,13 @@ internal static NormalizingEstimator Normalize(this TransformsCatalog catalog,
         }
 
         /// <summary>
-        /// It normalizes the data based on the observed minimum and maximum values of the data.
+        /// Create a <see cref="NormalizingEstimator"/>, which normalizes based on the observed minimum and maximum values of the data.
         /// </summary>
         /// <param name="catalog">The transform catalog</param>
-        /// <param name="outputColumnName">Name of the column resulting from the transformation of <paramref name="inputColumnName"/>.</param>
-        /// <param name="inputColumnName">Name of the column to transform. If set to <see langword="null"/>, the value of the <paramref name="outputColumnName"/> will be used as source.</param>
+        /// <param name="outputColumnName">Name of the column resulting from the transformation of <paramref name="inputColumnName"/>.
+        ///                                The data type on this column is the same as the input column.</param>
+        /// <param name="inputColumnName">Name of the column to transform. If set to <see langword="null"/>, the value of the <paramref name="outputColumnName"/> will be used as source.
+        ///                               The data type on this column should be <see cref="System.Single"/>, <see cref="System.Double"/> or a known-sized vector of those types.</param>
         /// <param name="maximumExampleCount">Maximum number of examples used to train the normalizer.</param>
         /// <param name="fixZero">Whether to map zero to zero, preserving sparsity.</param>
         /// <example>
@@ -55,10 +57,12 @@ public static NormalizingEstimator NormalizeMinMax(this TransformsCatalog catalo
         }
 
         /// <summary>
-        /// It normalizes the data based on the observed minimum and maximum values of the data.
+        /// Create a <see cref="NormalizingEstimator"/>, which normalizes based on the observed minimum and maximum values of the data.
         /// </summary>
         /// <param name="catalog">The transform catalog</param>
-        /// <param name="columns">List of Output and Input column pairs.</param>
+        /// <param name="columns">The pairs of input and output columns.
+        ///             The input columns must be of data type <see cref="System.Single"/>, <see cref="System.Double"/> or a known-sized vector of those types.
+        ///             The data type for the output column will be the same as the associated input column.</param>
         /// <param name="maximumExampleCount">Maximum number of examples used to train the normalizer.</param>
         /// <param name="fixZero">Whether to map zero to zero, preserving sparsity.</param>
         public static NormalizingEstimator NormalizeMinMax(this TransformsCatalog catalog, InputOutputColumnPair[] columns,
@@ -69,11 +73,13 @@ public static NormalizingEstimator NormalizeMinMax(this TransformsCatalog catalo
                     new NormalizingEstimator.MinMaxColumnOptions(column.OutputColumnName, column.InputColumnName, maximumExampleCount, fixZero)).ToArray());
 
         /// <summary>
-        /// It normalizes the data based on the computed mean and variance of the data.
+        /// Create a <see cref="NormalizingEstimator"/>, which normalizes based on the computed mean and variance of the data.
         /// </summary>
         /// <param name="catalog">The transform catalog</param>
-        /// <param name="outputColumnName">Name of the column resulting from the transformation of <paramref name="inputColumnName"/>.</param>
-        /// <param name="inputColumnName">Name of the column to transform. If set to <see langword="null"/>, the value of the <paramref name="outputColumnName"/> will be used as source.</param>
+        /// <param name="outputColumnName">Name of the column resulting from the transformation of <paramref name="inputColumnName"/>.
+        ///                                The data type on this column is the same as the input column.</param>
+        /// <param name="inputColumnName">Name of the column to transform. If set to <see langword="null"/>, the value of the <paramref name="outputColumnName"/> will be used as source.
+        ///                               The data type on this column should be <see cref="System.Single"/>, <see cref="System.Double"/> or a known-sized vector of those types.</param>
         /// <param name="maximumExampleCount">Maximum number of examples used to train the normalizer.</param>
         /// <param name="fixZero">Whether to map zero to zero, preserving sparsity.</param>
         /// <param name="useCdf">Whether to use CDF as the output.</param>
@@ -95,10 +101,12 @@ public static NormalizingEstimator NormalizeMeanVariance(this TransformsCatalog
         }
 
         /// <summary>
-        /// It normalizes the data based on the computed mean and variance of the data.
+        /// Create a <see cref="NormalizingEstimator"/>, which normalizes based on the computed mean and variance of the data.
         /// </summary>
         /// <param name="catalog">The transform catalog</param>
-        /// <param name="columns">List of Output and Input column pairs.</param>
+        /// <param name="columns">The pairs of input and output columns.
+        ///             The input columns must be of data type <see cref="System.Single"/>, <see cref="System.Double"/> or a known-sized vector of those types.
+        ///             The data type for the output column will be the same as the associated input column.</param>
         /// <param name="maximumExampleCount">Maximum number of examples used to train the normalizer.</param>
         /// <param name="fixZero">Whether to map zero to zero, preserving sparsity.</param>
         /// <param name="useCdf">Whether to use CDF as the output.</param>
@@ -111,11 +119,13 @@ public static NormalizingEstimator NormalizeMeanVariance(this TransformsCatalog
                         new NormalizingEstimator.MeanVarianceColumnOptions(column.OutputColumnName, column.InputColumnName, maximumExampleCount, fixZero, useCdf)).ToArray());
 
         /// <summary>
-        /// It normalizes the data based on the computed mean and variance of the logarithm of the data.
+        /// Create a <see cref="NormalizingEstimator"/>, which normalizes based on the computed mean and variance of the logarithm of the data.
         /// </summary>
         /// <param name="catalog">The transform catalog</param>
-        /// <param name="outputColumnName">Name of the column resulting from the transformation of <paramref name="inputColumnName"/>.</param>
-        /// <param name="inputColumnName">Name of the column to transform. If set to <see langword="null"/>, the value of the <paramref name="outputColumnName"/> will be used as source.</param>
+        /// <param name="outputColumnName">Name of the column resulting from the transformation of <paramref name="inputColumnName"/>.
+        ///                                The data type on this column is the same as the input column.</param>
+        /// <param name="inputColumnName">Name of the column to transform. If set to <see langword="null"/>, the value of the <paramref name="outputColumnName"/> will be used as source.
+        ///                               The data type on this column should be <see cref="System.Single"/>, <see cref="System.Double"/> or a known-sized vector of those types.</param>
         /// <param name="maximumExampleCount">Maximum number of examples used to train the normalizer.</param>
         /// <param name="useCdf">Whether to use CDF as the output.</param>
         /// <example>
@@ -135,10 +145,12 @@ public static NormalizingEstimator NormalizeLogMeanVariance(this TransformsCatal
         }
 
         /// <summary>
-        /// It normalizes the data based on the computed mean and variance of the logarithm of the data.
+        /// Create a <see cref="NormalizingEstimator"/>, which normalizes based on the computed mean and variance of the logarithm of the data.
         /// </summary>
         /// <param name="catalog">The transform catalog</param>
-        /// <param name="columns">List of Output and Input column pairs.</param>
+        /// <param name="columns">The pairs of input and output columns.
+        ///             The input columns must be of data type <see cref="System.Single"/>, <see cref="System.Double"/> or a known-sized vector of those types.
+        ///             The data type for the output column will be the same as the associated input column.</param>
         /// <param name="maximumExampleCount">Maximum number of examples used to train the normalizer.</param>
         /// <param name="useCdf">Whether to use CDF as the output.</param>
         public static NormalizingEstimator NormalizeLogMeanVariance(this TransformsCatalog catalog, InputOutputColumnPair[] columns,
@@ -149,11 +161,13 @@ public static NormalizingEstimator NormalizeLogMeanVariance(this TransformsCatal
                     new NormalizingEstimator.LogMeanVarianceColumnOptions(column.OutputColumnName, column.InputColumnName, maximumExampleCount, useCdf)).ToArray());
 
         /// <summary>
-        /// The values are assigned into bins with equal density.
+        /// Create a <see cref="NormalizingEstimator"/>, which normalizes by assigning the data into bins with equal density.
         /// </summary>
         /// <param name="catalog">The transform catalog</param>
-        /// <param name="outputColumnName">Name of the column resulting from the transformation of <paramref name="inputColumnName"/>.</param>
-        /// <param name="inputColumnName">Name of the column to transform. If set to <see langword="null"/>, the value of the <paramref name="outputColumnName"/> will be used as source.</param>
+        /// <param name="outputColumnName">Name of the column resulting from the transformation of <paramref name="inputColumnName"/>.
+        ///                                The data type on this column is the same as the input column.</param>
+        /// <param name="inputColumnName">Name of the column to transform. If set to <see langword="null"/>, the value of the <paramref name="outputColumnName"/> will be used as source.
+        ///                               The data type on this column should be <see cref="System.Single"/>, <see cref="System.Double"/> or a known-sized vector of those types.</param>
         /// <param name="maximumExampleCount">Maximum number of examples used to train the normalizer.</param>
         /// <param name="fixZero">Whether to map zero to zero, preserving sparsity.</param>
         /// <param name="maximumBinCount">Maximum number of bins (power of 2 recommended).</param>
@@ -175,10 +189,12 @@ public static NormalizingEstimator NormalizeBinning(this TransformsCatalog catal
         }
 
         /// <summary>
-        /// The values are assigned into bins with equal density.
+        /// Create a <see cref="NormalizingEstimator"/>, which normalizes by assigning the data into bins with equal density.
         /// </summary>
         /// <param name="catalog">The transform catalog</param>
-        /// <param name="columns">List of Output and Input column pairs.</param>
+        /// <param name="columns">The pairs of input and output columns.
+        ///             The input columns must be of data type <see cref="System.Single"/>, <see cref="System.Double"/> or a known-sized vector of those types.
+        ///             The data type for the output column will be the same as the associated input column.</param>
         /// <param name="maximumExampleCount">Maximum number of examples used to train the normalizer.</param>
         /// <param name="fixZero">Whether to map zero to zero, preserving sparsity.</param>
         /// <param name="maximumBinCount">Maximum number of bins (power of 2 recommended).</param>
@@ -191,11 +207,13 @@ public static NormalizingEstimator NormalizeBinning(this TransformsCatalog catal
                     new NormalizingEstimator.BinningColumnOptions(column.OutputColumnName, column.InputColumnName, maximumExampleCount, fixZero, maximumBinCount)).ToArray());
 
         /// <summary>
-        /// The values are assigned into bins based on correlation with the <paramref name="labelColumnName"/> column.
+        /// Create a <see cref="NormalizingEstimator"/>, which normalizes by assigning the data into bins based on correlation with the <paramref name="labelColumnName"/> column.
         /// </summary>
         /// <param name="catalog">The transform catalog</param>
-        /// <param name="outputColumnName">Name of the column resulting from the transformation of <paramref name="inputColumnName"/>.</param>
-        /// <param name="inputColumnName">Name of the column to transform. If set to <see langword="null"/>, the value of the <paramref name="outputColumnName"/> will be used as source.</param>
+        /// <param name="outputColumnName">Name of the column resulting from the transformation of <paramref name="inputColumnName"/>.
+        ///                                The data type on this column is the same as the input column.</param>
+        /// <param name="inputColumnName">Name of the column to transform. If set to <see langword="null"/>, the value of the <paramref name="outputColumnName"/> will be used as source.
+        ///                               The data type on this column should be <see cref="System.Single"/>, <see cref="System.Double"/> or a known-sized vector of those types.</param>
         /// <param name="labelColumnName">Name of the label column for supervised binning.</param>
         /// <param name="maximumExampleCount">Maximum number of examples used to train the normalizer.</param>
         /// <param name="fixZero">Whether to map zero to zero, preserving sparsity.</param>
@@ -221,10 +239,12 @@ public static NormalizingEstimator NormalizeSupervisedBinning(this TransformsCat
         }
 
         /// <summary>
-        /// The values are assigned into bins based on correlation with the <paramref name="labelColumnName"/> column.
+        /// Create a <see cref="NormalizingEstimator"/>, which normalizes by assigning the data into bins based on correlation with the <paramref name="labelColumnName"/> column.
         /// </summary>
         /// <param name="catalog">The transform catalog</param>
-        /// <param name="columns">List of Output and Input column pairs.</param>
+        /// <param name="columns">The pairs of input and output columns.
+        ///             The input columns must be of data type <see cref="System.Single"/>, <see cref="System.Double"/> or a known-sized vector of those types.
+        ///             The data type for the output column will be the same as the associated input column.</param>
         /// <param name="labelColumnName">Name of the label column for supervised binning.</param>
         /// <param name="maximumExampleCount">Maximum number of examples used to train the normalizer.</param>
         /// <param name="fixZero">Whether to map zero to zero, preserving sparsity.</param>
@@ -256,7 +276,8 @@ internal static NormalizingEstimator Normalize(this TransformsCatalog catalog,
         /// a pre-processing step would be applied to make the specified column's mean be a zero vector.
         /// </summary>
         /// <param name="catalog">The transform's catalog.</param>
-        /// <param name="outputColumnName">Name of the column resulting from the transformation of <paramref name="inputColumnName"/>.</param>
+        /// <param name="outputColumnName">Name of the column resulting from the transformation of <paramref name="inputColumnName"/>.
+        ///                                The data type on this column is the same as the input column.</param>
         /// <param name="inputColumnName">Name of column to transform. If set to <see langword="null"/>, the value of the <paramref name="outputColumnName"/> will be used as source.</param>
         /// <param name="norm">Type of norm to use to normalize each sample. The indicated norm of the resulted vector will be normalized to one.</param>
         /// <param name="ensureZeroMean">If <see langword="true"/>, subtract mean from each value before normalizing and use the raw input otherwise.</param>
@@ -290,7 +311,8 @@ internal static LpNormNormalizingEstimator NormalizeLpNorm(this TransformsCatalo
         /// a pre-processing step would be applied to make the specified column's mean be a zero vector.
         /// </summary>
         /// <param name="catalog">The transform's catalog.</param>
-        /// <param name="outputColumnName">Name of the column resulting from the transformation of <paramref name="inputColumnName"/>.</param>
+        /// <param name="outputColumnName">Name of the column resulting from the transformation of <paramref name="inputColumnName"/>.
+        ///                                The data type on this column is the same as the input column.</param>
         /// <param name="inputColumnName">Name of column to transform. If set to <see langword="null"/>, the value of the <paramref name="outputColumnName"/> will be used as source.</param>
         /// <param name="ensureZeroMean">If <see langword="true"/>, subtract mean from each value before normalizing and use the raw input otherwise.</param>
         /// <param name="ensureUnitStandardDeviation">If <see langword="true"/>, resulted vector's standard deviation would be one. Otherwise, resulted vector's L2-norm would be one.</param>