Model robustness refers to a model's ability to maintain its performance when faced with variations in input data or changes in the environment. This means that a robust model can handle unexpected situations, such as noise in the data or shifts in underlying patterns, without significant degradation in accuracy. In machine learning, achieving robustness often involves techniques like data augmentation, regularization, and cross-validation. These methods help ensure that the model generalizes well to new, unseen data, making it more reliable and effective in real-world applications.