Filter methods, which select features based on their individual statistical properties, such as correlation with the target variable.

Wrapper methods, which select features by evaluating their performance in a specific machine learning model.

Embedded methods, which select features by incorporating them into the model training process.

All of the above, as Spark MLlib supports various feature selection techniques.