Model interpretability refers to the ability to understand, explain, and trust the decisions made by machine learning models, which is crucial for ensuring transparency, accountability, and fairness. It involves techniques and tools that make the model's predictions and inner workings comprehensible to humans, facilitating better decision-making and debugging.