LIME provides local interpretable explanations for classifiers across text, tabular, and image data. It generates human-understandable explanations by sampling around individual predictions and fitting interpretable models. The library is model-agnostic and works with any classifier that outputs class probabilities, with built-in support for scikit-learn models.