Overfitting is a common challenge in machine learning where a model learns the training data to an extent that it captures noise, outliers, and specific patterns that do not generalize to new, unseen data. While an overfitted model may perform exceptionally well on training data, it typically exhibits poor performance during validation or testing, failing to make accurate predictions on real-world scenarios. To mitigate overfitting, various techniques can be employed, including regularization (L1 and L2), cross-validation, and early stopping. Additionally, simplifying the model architecture or increasing the size of the training dataset can help achieve a balance between bias and variance, enhancing the model's generalization capability.