What is the primary issue caused by overfitting in machine learning models?

Overfitting in machine learning occurs when a model learns not only the underlying patterns in the training data but also the noise or random fluctuations. This means the model is too complex, having too many parameters relative to the amount of training data, resulting in a situation where it performs exceptionally well on the training set but poorly on new, unseen data.

When a model captures these random fluctuations, it essentially memorizes the training data rather than generalizing from it. Consequently, while the model may show excellent accuracy on the training dataset, its predictive power diminishes significantly when evaluating real-world data or data it hasn't encountered before. This inability to generalize reflects a fundamental issue with overfitting, making it a primary concern in developing robust machine learning models.