Data cleaning, which involves handling missing values, outliers, and errors in the dataset.

Data transformation, which involves converting the data into a suitable format for machine learning models, such as normalization or standardization.

Feature engineering, which involves creating new features from existing data to improve the model's performance.

All of the above, as Spark MLlib supports various data preprocessing techniques.