Recursive partitioning is a statistical method used to split data into subsets based on feature values, often visualized as decision trees, to facilitate prediction or classification. It iteratively divides data to optimize a certain criterion, such as minimizing variance or maximizing information gain, leading to a model that is easy to interpret and implement.