diff --git a/src/Microsoft.ML.StandardLearners/Standard/MultiClass/Ova.cs b/src/Microsoft.ML.StandardLearners/Standard/MultiClass/Ova.cs
index 895dcea7cf..3d6e1e67b2 100644
--- a/src/Microsoft.ML.StandardLearners/Standard/MultiClass/Ova.cs
+++ b/src/Microsoft.ML.StandardLearners/Standard/MultiClass/Ova.cs
@@ -200,10 +200,13 @@ public static ModelOperations.PredictorModelOutput CombineOvaModels(IHostEnviron
             host.CheckValue(input, nameof(input));
             EntryPointUtils.CheckInputArgs(host, input);
             host.CheckNonEmpty(input.ModelArray, nameof(input.ModelArray));
-
+            // Something tells me we should put normalization as part of macro expansion, but since i get 
+            // subgraph instead of learner it's a bit tricky to get learner and decide should we add 
+            // normalization node or not, plus everywhere in code we leave that reposnsibility to TransformModel.
+            var normalizedView = input.ModelArray[0].TransformModel.Apply(host, input.TrainingData);
             using (var ch = host.Start("CombineOvaModels"))
             {
-                ISchema schema = input.TrainingData.Schema;
+                ISchema schema = normalizedView.Schema;
                 var label = TrainUtils.MatchNameOrDefaultOrNull(ch, schema, nameof(input.LabelColumn),
                     input.LabelColumn,
                     DefaultColumnNames.Label);
@@ -211,7 +214,7 @@ public static ModelOperations.PredictorModelOutput CombineOvaModels(IHostEnviron
                     input.FeatureColumn, DefaultColumnNames.Features);
                 var weight = TrainUtils.MatchNameOrDefaultOrNull(ch, schema, nameof(input.WeightColumn),
                     input.WeightColumn, DefaultColumnNames.Weight);
-                var data = TrainUtils.CreateExamples(input.TrainingData, label, feature, null, weight);
+                var data = TrainUtils.CreateExamples(normalizedView, label, feature, null, weight);
 
                 return new ModelOperations.PredictorModelOutput
                 {
diff --git a/test/Microsoft.ML.Core.Tests/UnitTests/TestCSharpApi.cs b/test/Microsoft.ML.Core.Tests/UnitTests/TestCSharpApi.cs
index bbcee502d7..b42dee2d52 100644
--- a/test/Microsoft.ML.Core.Tests/UnitTests/TestCSharpApi.cs
+++ b/test/Microsoft.ML.Core.Tests/UnitTests/TestCSharpApi.cs
@@ -739,5 +739,64 @@ public void TestCrossValidationMacroWithNonDefaultNames()
                 }
             }
         }
+
+        [Fact]
+        public void TestOvaMacro()
+        {
+            var dataPath = GetDataPath(@"iris.txt");
+            using (var env = new TlcEnvironment(42))
+            {
+                // Specify subgraph for OVA
+                var subGraph = env.CreateExperiment();
+                var learnerInput = new Trainers.StochasticDualCoordinateAscentBinaryClassifier { NumThreads = 1 };
+                var learnerOutput = subGraph.Add(learnerInput);
+                // Create pipeline with OVA and multiclass scoring.
+                var experiment = env.CreateExperiment();
+                var importInput = new ML.Data.TextLoader(dataPath);
+                importInput.Arguments.Column = new TextLoaderColumn[]
+                {
+                    new TextLoaderColumn { Name = "Label", Source = new[] { new TextLoaderRange(0) } },
+                    new TextLoaderColumn { Name = "Features", Source = new[] { new TextLoaderRange(1,4) } }
+                };
+                var importOutput = experiment.Add(importInput);
+                var oneVersusAll = new Models.OneVersusAll
+                {
+                    TrainingData = importOutput.Data,
+                    Nodes = subGraph,
+                    UseProbabilities = true,
+                };
+                var ovaOutput = experiment.Add(oneVersusAll);
+                var scoreInput = new ML.Transforms.DatasetScorer
+                {
+                    Data = importOutput.Data,
+                    PredictorModel = ovaOutput.PredictorModel
+                };
+                var scoreOutput = experiment.Add(scoreInput);
+                var evalInput = new ML.Models.ClassificationEvaluator
+                {
+                    Data = scoreOutput.ScoredData
+                };
+                var evalOutput = experiment.Add(evalInput);
+                experiment.Compile();
+                experiment.SetInput(importInput.InputFile, new SimpleFileHandle(env, dataPath, false, false));
+                experiment.Run();
+
+                var data = experiment.GetOutput(evalOutput.OverallMetrics);
+                var schema = data.Schema;
+                var b = schema.TryGetColumnIndex(MultiClassClassifierEvaluator.AccuracyMacro, out int accCol);
+                Assert.True(b);
+                using (var cursor = data.GetRowCursor(col => col == accCol))
+                {
+                    var getter = cursor.GetGetter<double>(accCol);
+                    b = cursor.MoveNext();
+                    Assert.True(b);
+                    double acc = 0;
+                    getter(ref acc);
+                    Assert.Equal(0.96, acc, 2);
+                    b = cursor.MoveNext();
+                    Assert.False(b);
+                }
+            }
+        }
     }
 }