<human>:  Explain the concept of cross-validation in machine learning.
<bot>: Cross-validation is a technique used in machine learning to evaluate the performance of a model on a dataset. It works by splitting the dataset into multiple partitions and then training and testing the model on different partitions. The results from the different partitions are then combined to obtain an overall estimate of the model performance. This helps to avoid overfitting, which is when a model performs well on a training set, but poorly on a test set. Cross-validation can be used to optimize model parameters and detect outliers in data. It is also useful in assessing the generalization of a model, meaning how well it can predict unseen data points.