In previous chapters, we addressed data preprocessing and model training as distinct steps. Applying these operations sequentially, especially within cross-validation loops, can be cumbersome and risks inadvertently leaking information from the test fold into the training process.

This chapter introduces Scikit-learn's Pipeline object, a tool designed to chain multiple processing steps (like scalers, encoders, and imputers) together with a final estimator (like a classifier or regressor). You will learn how to construct these pipelines to create a single object representing your entire modeling workflow. We will cover how to integrate pipelines effectively with cross-validation and hyperparameter tuning using GridSearchCV, ensuring preprocessing is applied correctly within each fold. Finally, we will look at using ColumnTransformer to build more complex pipelines that apply different transformations to different subsets of columns in your dataset.