What issue occurs when a model is too complex and matches the training data too closely?

When a model is too complex and fits the training data too closely, it leads to overfitting. This occurs when the model captures not only the underlying trend but also the noise and fluctuations present in the training data. As a result, the model performs exceptionally well on the training set, but its performance significantly deteriorates on unseen data or a validation set. This happens because overfitted models lack the ability to generalize, which is crucial for predicting new data effectively.

In contrast, generalization refers to the model's ability to perform well on new, unseen data rather than just the data it was trained on. Selection bias relates to the systematic differences in the data used for training, potentially leading to inaccurate conclusions. Reporting bias involves selective reporting of results in a way that skews the interpretation of data. Understanding overfitting is key in data science, as preventing it ensures that models remain robust and applicable to real-world scenarios.