Partial Least Squares (PLS) is a statistical method used to model relationships between variables. It is particularly useful when dealing with datasets that have many predictors and fewer observations. PLS reduces the dimensionality of the data by creating new variables, called latent variables, which capture the most important information from the original predictors. This technique is commonly applied in fields like chemometrics, social sciences, and bioinformatics. PLS helps in predicting outcomes and understanding complex relationships, making it a valuable tool for researchers and analysts who need to interpret large datasets effectively.