Uniform stability in machine learning and statistical learning theory refers to the property where a learning algorithm demonstrates consistent performance across different datasets, ensuring that its generalization error does not significantly vary with small changes in the training data. This concept is crucial for guaranteeing the reliability and robustness of the model's predictions in practical, varied scenarios.