Can you explain the concept of cross-validation and its importance in statistical analysis?

Cross-validation is a technique used in statistical analysis to evaluate the performance of a predictive model. It involves dividing the data into subsets, training the model on some of the subsets, and then testing it on the remaining subset. This process is repeated multiple times to ensure the model's accuracy and generalizability. Cross-validation is important because it helps to assess how well a model will perform on new, unseen data, and can help prevent overfitting or underfitting of the model.