Spark ML Pipeline with RandomForest takes too long on 20MB dataset

I am using Spark ML to run some ML experiments, and on a small dataset of 20MB (Poker dataset) and a Random Forest with parameter grid, it takes 1h and 30 minutes to finish. Similarly with scikit-learn it takes much much less.

In terms of environment, I was testing with 2 slaves, 15GB memory each, 24 cores. I assume it was not supposed to take that long and I am wondering if the problem lies within my code, since I am fairly new to Spark.

Here it is:

df = pd.read_csv(http://archive.ics.uci.edu/ml/machine-learning-databases/poker/poker-hand-testing.data)
dataframe = sqlContext.createDataFrame(df)

train, test = dataframe.randomSplit([0.7, 0.3])

columnTypes = dataframe.dtypes

for ct in columnTypes:
    if ct[1] == 'string' and ct[0] != 'label':
        categoricalCols += [ct[0]]
    elif ct[0] != 'label':
        numericCols += [ct[0]]

stages = []

for categoricalCol in categoricalCols:

    stringIndexer = StringIndexer(inputCol=categoricalCol, outputCol=categoricalCol+"Index")

stages += [stringIndexer]

assemblerInputs = map(lambda c: c + "Index", categoricalCols) + numericCols

assembler = VectorAssembler(inputCols=assemblerInputs, outputCol="features")

stages += [assembler]

labelIndexer = StringIndexer(inputCol='label', outputCol='indexedLabel', handleInvalid='skip')

stages += [labelIndexer]

estimator = RandomForestClassifier(labelCol="indexedLabel", featuresCol="features")

stages += [estimator]

parameters = {"maxDepth" : [3, 5, 10, 15], "maxBins" : [6, 12, 24, 32], "numTrees" : [3, 5, 10]}

paramGrid = ParamGridBuilder()
for key, value in parameters.iteritems():
    paramGrid.addGrid(estimator.getParam(key), value)
estimatorParamMaps = (paramGrid.build())

pipeline = Pipeline(stages=stages)

crossValidator = CrossValidator(estimator=pipeline, estimatorParamMaps=estimatorParamMaps, evaluator=MulticlassClassificationEvaluator(labelCol='indexedLabel', predictionCol='prediction', metricName='f1'), numFolds=3)

pipelineModel = crossValidator.fit(train)

predictions = pipelineModel.transform(test)

evaluator = pipeline.getEvaluator().evaluate(predictions)

Thanks in advance, any comments/suggestions are highly appreciated :)