Machine learning models improve over time through a process known as training and fine-tuning. They learn from data by adjusting internal parameters to minimize prediction errors. This iterative process involves feeding models large datasets, enabling them to recognize patterns and make informed predictions. Additionally, useful strategies like cross-validation and regularization help ensure models generalize well to new data. Continuous learning and adaptation to new information further enhance model accuracy and relevance.