What can be the effect of overfitting in polynomial regression?

Overfitting in polynomial regression means that the model fits the training data too closely, capturing not only the underlying pattern but also the noise. As a result, the model will perform well on the training data but poorly on new, unseen data. This is because the model has essentially 'memorized' the training data and fails to generalize well to new situations.