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 CrossValidator class from the pyspark.ml.tuning module to perform hyperparameter tuning by performing k-fold cross-validation, and selecting the best hyperparameters based on the average performance across all folds.

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.