A linear regression model's R-Squared value significantly improves after polynomial features are added. What could be the reason, and what should you be cautious about?

The significant improvement in R-Squared value after adding polynomial features indicates an improved fit to potentially nonlinear patterns in the data. However, caution should be exercised as adding too many polynomial features may lead to overfitting, where the model fits the noise in the training data rather than the underlying trend. Regularization techniques and cross-validation can be used to mitigate this risk.