Machine learning models adapt to new data through processes such as retraining and incremental learning. As new data becomes available, these models can be updated to improve their accuracy and relevance. Retraining involves using a more extensive dataset that includes both old and new data to refine the model's parameters. Incremental learning allows models to learn continuously from a stream of data, updating their knowledge without needing complete retraining. This adaptability sustains the model's performance in dynamic environments, ensuring they remain effective over time.