InterpretML provides state-of-the-art machine learning interpretability techniques for training interpretable glassbox models and explaining blackbox systems. It supports global and local model explanations, helping data scientists understand feature importance, model decisions, and detect fairness issues. The package integrates with scikit-learn and implements techniques like EBM (Explainable Boosting Machines) for interpretable predictions.