Algorithmic stability refers to the robustness of an algorithm's performance when subjected to small perturbations in its input data, which is crucial for ensuring generalization in machine learning models. It is a fundamental property that impacts the reliability and predictability of algorithms, particularly in terms of their ability to perform consistently across different datasets or under minor changes in training data.