Auto-sklearn automatically selects algorithms, tunes hyperparameters, and assembles models to build ML pipelines. It uses Bayesian optimization (via SMAC) and meta-learning to efficiently search the space of possible models and configurations. The library acts as a drop-in replacement for scikit-learn estimators, handling the full ML workflow from preprocessing to final model selection.