PySpark Crossvalidation error

I want to do a very simple cross validation using LogisticRegression.
Here is my code:

logreg = LogisticRegression(labelCol = "churn", featuresCol = "features") 

pipeline = Pipeline(stages = [logreg])
paramGrid = ParamGridBuilder().addGrid(logreg.regParam, [.1, .01]).build()

crossval = CrossValidator(
    estimator = pipeline,
    estimatorParamMaps = paramGrid,
    evaluator = BinaryClassificationEvaluator(),
    numFolds = 2)

bestLogReg = crossval.fit(df_train)

When I run this, I get the following error on bestLogReg = crossval.fit(df_train):

IllegalArgumentException: label does not exist. Available: features, churn, CrossValidator_764038c00edc_rand, rawPrediction, probability, prediction

Here is my df_train dataset’s schema:

root
 |-- features: vector (nullable = true)
 |-- churn: integer (nullable = true)

I have fit this to a LogisticRegression before and it predicts fine.

Can you help me figure out what I did wrong?