Random forests are a machine learning ensemble method that consists of multiple decision trees, each trained on a random subset of the data and features. The predictions of individual trees are then aggregated to produce a final prediction. Random forests are known for their high accuracy, robustness, and ability to handle large datasets with high dimensionality. They are commonly used for classification and regression tasks in various fields such as finance, healthcare, and marketing.