Cross-validation is a technique for assessing how the results of a statistical analysis will generalize to an independent data set. It involves partitioning the original data set into a training set to train the model and a validation set to test the model. The process is repeated multiple times with different partitions to ensure that the model's performance is consistent and reliable. Cross-validation helps in detecting overfitting and underfitting, providing a more accurate estimate of a model's predictive performance. It is widely used in machine learning to validate the effectiveness of models before deploying them in real-world scenarios.