diff --git a/src/Microsoft.ML/Runtime/EntryPoints/TrainTestSplit.cs b/src/Microsoft.ML/Runtime/EntryPoints/TrainTestSplit.cs
index 8b199045fa..40909ad108 100644
--- a/src/Microsoft.ML/Runtime/EntryPoints/TrainTestSplit.cs
+++ b/src/Microsoft.ML/Runtime/EntryPoints/TrainTestSplit.cs
@@ -93,7 +93,8 @@ public static string CreateStratificationColumn(IHost host, ref IDataView data,
                     new HashJoinTransform.Arguments
                     {
                         Column = new[] { new HashJoinTransform.Column { Name = stratCol, Source = stratificationColumn } },
-                        Join = true
+                        Join = true,
+                        HashBits = 30
                     }, data);
             }
 
diff --git a/test/Microsoft.ML.Core.Tests/UnitTests/TestCSharpApi.cs b/test/Microsoft.ML.Core.Tests/UnitTests/TestCSharpApi.cs
index 66e241163f..439dc069f4 100644
--- a/test/Microsoft.ML.Core.Tests/UnitTests/TestCSharpApi.cs
+++ b/test/Microsoft.ML.Core.Tests/UnitTests/TestCSharpApi.cs
@@ -330,5 +330,73 @@ public void TestCrossValidationMacro()
                 }
             }
         }
+
+        [Fact]
+        public void TestCrossValidationMacroWithStratification()
+        {
+            var dataPath = GetDataPath(@"breast-cancer.txt");
+            using (var env = new TlcEnvironment())
+            {
+                var subGraph = env.CreateExperiment();
+
+                var nop = new ML.Transforms.NoOperation();
+                var nopOutput = subGraph.Add(nop);
+
+                var learnerInput = new ML.Trainers.StochasticDualCoordinateAscentBinaryClassifier
+                {
+                    TrainingData = nopOutput.OutputData,
+                    NumThreads = 1
+                };
+                var learnerOutput = subGraph.Add(learnerInput);
+
+                var modelCombine = new ML.Transforms.ManyHeterogeneousModelCombiner
+                {
+                    TransformModels = new ArrayVar<ITransformModel>(nopOutput.Model),
+                    PredictorModel = learnerOutput.PredictorModel
+                };
+                var modelCombineOutput = subGraph.Add(modelCombine);
+
+                var experiment = env.CreateExperiment();
+                var importInput = new ML.Data.TextLoader(dataPath);
+                importInput.Arguments.Column = new ML.Data.TextLoaderColumn[]
+                {
+                    new ML.Data.TextLoaderColumn { Name = "Label", Source = new[] { new ML.Data.TextLoaderRange(0) } },
+                    new ML.Data.TextLoaderColumn { Name = "Strat", Source = new[] { new ML.Data.TextLoaderRange(1) } },
+                    new ML.Data.TextLoaderColumn { Name = "Features", Source = new[] { new ML.Data.TextLoaderRange(2, 9) } }
+                };
+                var importOutput = experiment.Add(importInput);
+
+                var crossValidate = new ML.Models.CrossValidator
+                {
+                    Data = importOutput.Data,
+                    Nodes = subGraph,
+                    TransformModel = null,
+                    StratificationColumn = "Strat"
+                };
+                crossValidate.Inputs.Data = nop.Data;
+                crossValidate.Outputs.Model = modelCombineOutput.PredictorModel;
+                var crossValidateOutput = experiment.Add(crossValidate);
+
+                experiment.Compile();
+                experiment.SetInput(importInput.InputFile, new SimpleFileHandle(env, dataPath, false, false));
+                experiment.Run();
+                var data = experiment.GetOutput(crossValidateOutput.OverallMetrics[0]);
+
+                var schema = data.Schema;
+                var b = schema.TryGetColumnIndex("AUC", out int metricCol);
+                Assert.True(b);
+                using (var cursor = data.GetRowCursor(col => col == metricCol))
+                {
+                    var getter = cursor.GetGetter<double>(metricCol);
+                    b = cursor.MoveNext();
+                    Assert.True(b);
+                    double val = 0;
+                    getter(ref val);
+                    Assert.Equal(0.99, val, 2);
+                    b = cursor.MoveNext();
+                    Assert.False(b);
+                }
+            }
+        }
     }
 }