Dataset augmentation is a technique used to artificially expand the size and diversity of a dataset by applying various transformations to the data, which helps improve the robustness and generalization ability of machine learning models. By simulating variations that might be encountered in real-world scenarios, it allows models to learn more invariant features and reduces the risk of overfitting.