diff --git a/python/pyspark/sql/pandas/serializers.py b/python/pyspark/sql/pandas/serializers.py
index 6203d4d..0762268 100644
--- a/python/pyspark/sql/pandas/serializers.py
+++ b/python/pyspark/sql/pandas/serializers.py
@@ -510,8 +510,8 @@
                 # If it returns a pd.Series, it should throw an error.
                 if not isinstance(s, pd.DataFrame):
                     raise PySparkValueError(
-                        "A field of type StructType expects a pandas.DataFrame, "
-                        "but got: %s" % str(type(s))
+                        "Invalid return type. Please make sure that the UDF returns a "
+                        "pandas.DataFrame when the specified return type is StructType."
                     )
                 arrs.append(self._create_struct_array(s, t))
             else:
diff --git a/python/pyspark/sql/tests/pandas/test_pandas_udf.py b/python/pyspark/sql/tests/pandas/test_pandas_udf.py
index 6720dfc..228fc30 100644
--- a/python/pyspark/sql/tests/pandas/test_pandas_udf.py
+++ b/python/pyspark/sql/tests/pandas/test_pandas_udf.py
@@ -339,6 +339,19 @@
         self.assertEqual(df.schema[0].dataType.simpleString(), "interval day to second")
         self.assertEqual(df.first()[0], datetime.timedelta(microseconds=123))
 
+    def test_pandas_udf_return_type_error(self):
+        import pandas as pd
+
+        @pandas_udf("s string")
+        def upper(s: pd.Series) -> pd.Series:
+            return s.str.upper()
+
+        df = self.spark.createDataFrame([("a",)], schema="s string")
+
+        self.assertRaisesRegex(
+            PythonException, "Invalid return type", df.select(upper("s")).collect
+        )
+
 
 class PandasUDFTests(PandasUDFTestsMixin, ReusedSQLTestCase):
     pass