PCA, or Principal Component Analysis, is a statistical technique used to simplify complex datasets. It reduces the number of variables while retaining the most important information. By transforming the original data into a new set of variables called principal components, PCA helps in identifying patterns and relationships within the data. This method is widely used in various fields, including machine learning, image processing, and finance. PCA can enhance data visualization and improve the performance of algorithms by eliminating noise and redundancy, making it easier to analyze and interpret large datasets.