imbalanced-learn is a Python package that provides re-sampling algorithms commonly used to tackle the curse of imbalanced datasets in machine learning. It offers techniques like SMOTE, ADASYN, and various under-sampling methods to modify class distributions for improved model training. The library is fully compatible with scikit-learn estimators and integrates with the broader scikit-learn ecosystem.