Overfitting in machine learning models occurs when a model captures noise instead of the underlying data distribution. To mitigate overfitting, techniques like regularization (L1, L2), dropout, data augmentation, early stopping, and learning rate adjustments are essential. Monitoring training metrics helps visualize model performance.