Overfitting occurs when the model is too complex and captures noise instead of the underlying pattern, leading to poor performance on new data. Underfitting happens when the model is too simple and fails to capture the important trends. Both issues highlight the importance of selecting an appropriate model complexity and performing regularization techniques.
