Black-box optimization refers to optimizing an objective function without explicit knowledge of its internal workings, often relying solely on input-output evaluations. It is particularly useful in scenarios where the function is complex, expensive to evaluate, or lacks a closed-form expression, such as in machine learning hyperparameter tuning or engineering design problems.