Overfitting is a common challenge in machine learning where a model learns to perform exceptionally well on its training data but fails to generalize to new, unseen data. This occurs when the model captures noise and random fluctuations in the training dataset rather than the underlying patterns. As a result, while the model may show high accuracy during training, its performance deteriorates significantly when applied to real-world scenarios. Understanding and addressing overfitting is crucial for developing reliable machine learning models that can make accurate predictions in practical applications. Techniques such as regularization and cross-validation are often employed to mitigate this issue.