Cross-validation is a valuable technique in model evaluation that helps to assess the performance of a machine learning model in a more robust and reliable manner. It involves splitting the available data into multiple subsets or folds, training the model on a subset, and evaluating its performance on the remaining fold. This process is repeated several times to obtain an average evaluation score.

Cross-validation offers several benefits. Firstly, it helps to reduce the chance of overfitting by providing a more comprehensive evaluation of the model on various data subsets. Secondly, it allows us to utilize all available data for both training and evaluation, leading to a more efficient use of the dataset. Lastly, it provides valuable insights into the model's generalization capabilities.

One popular method of cross-validation is k-fold cross-validation, where the dataset is divided into k equal-sized folds. The model is trained and evaluated k times, each time using a different fold as the evaluation set while the rest act as the training set. Another variation is leave-one-out cross-validation, where k is set to the number of samples in the data, resulting in each sample being used as the evaluation set once.

Implementing cross-validation in code is fairly straightforward. Libraries such as scikit-learn provide built-in functions for performing cross-validation with various strategies. By utilizing cross-validation, you can gain valuable insights into your model's performance and make more informed decisions when selecting and fine-tuning your machine learning models.