Use the TrainValidationSplit class from the pyspark.ml.tuning module to perform hyperparameter tuning by splitting the data into training and validation sets, and selecting the best hyperparameters based on the model's performance on the validation set.

Use the RandomForestRegressor class from the pyspark.ml.regression module with default hyperparameters and train the model without performing hyperparameter tuning.

Use the LogisticRegression class from the pyspark.ml.classification module with default hyperparameters and train the model without performing hyperparameter tuning.