diff --git a/python/cuml/cuml/_thirdparty/sklearn/preprocessing/_column_transformer.py b/python/cuml/cuml/_thirdparty/sklearn/preprocessing/_column_transformer.py
index 466bd5da3d..8357d8ddad 100644
--- a/python/cuml/cuml/_thirdparty/sklearn/preprocessing/_column_transformer.py
+++ b/python/cuml/cuml/_thirdparty/sklearn/preprocessing/_column_transformer.py
@@ -738,6 +738,15 @@ def _validate_remainder(self, X):
         if hasattr(X, 'columns'):
             self._df_columns = X.columns
 
+        if hasattr(X, "shape"):
+            self._n_features = X.shape[1]
+        elif hasattr(X, "__len__") and len(X) > 0 and hasattr(X[0], "__len__"):
+            self._n_features = len(X[0])
+        else:
+            raise TypeError(
+                "Input 'X' must be a 2D array, dataframe, or rectangular nested sequence."
+            )
+
         self._n_features = X.shape[1]
         cols = []
         for columns in self._columns:
diff --git a/python/cuml/tests/test_compose.py b/python/cuml/tests/test_compose.py
index 665ad12b8a..d50f7e50f6 100644
--- a/python/cuml/tests/test_compose.py
+++ b/python/cuml/tests/test_compose.py
@@ -383,3 +383,15 @@ def test_column_transform_properly_handles_sub_output_type():
         ]
     ).fit(df)
     transformer.transform(df)
+
+def test_make_column_transformer_list_input():
+    from cuml.compose import make_column_transformer
+    from cuml.preprocessing import StandardScaler
+    import numpy as np
+
+    a = [[1, 2], [3, 4], [5, 6]]
+    transformer = make_column_transformer((StandardScaler(), [0]))
+    res = transformer.fit_transform(a)
+
+    expected = np.array([[-1.22474487], [0.0], [1.22474487]])
+    np.testing.assert_allclose(res, expected, rtol=1e-5, atol=1e-5)