Overfitting and underfitting are two critical concepts in machine learning that affect model performance. Overfitting occurs when a model learns not only the underlying patterns in the training data but also the noise, leading to poor generalization on unseen data. This results in high accuracy on the training set but low accuracy on the validation or test set. Conversely, underfitting happens when a model is too simplistic to capture the underlying trends, resulting in poor performance on both training and validation datasets. Understanding and addressing these issues is essential for developing effective machine learning models.