Machine learning algorithms improve over time through a process known as iteration and training. Initially, the algorithms are trained on a dataset to recognize patterns and make predictions. As they encounter more data, they can adjust their models by optimizing parameters and refining their understanding of the input-output relationships. This continuous feedback loop allows algorithms to learn from past predictions, reducing errors and enhancing performance. Techniques such as cross-validation and regularization further ensure that the algorithms generalize well beyond their training data, providing better accuracy over time.