Model debugging is a critical process in machine learning that involves identifying and resolving errors or inefficiencies in a model to improve its performance and reliability. It encompasses techniques such as error analysis, visualization, and testing to ensure the model's predictions align with expected outcomes and to understand the underlying reasons for any discrepancies.