Learning to Rank is a machine learning technique used to improve the order of search results. It involves training algorithms to understand which items are more relevant to a user's query based on various features, such as user behavior and content quality. This method is commonly applied in search engines and recommendation systems. The process typically involves creating a dataset of queries and their corresponding relevant items. Algorithms like Support Vector Machines or Gradient Boosting are then used to learn from this data, optimizing the ranking of results to enhance user satisfaction and engagement.