Principal Component Analysis (PCA) is a statistical technique used to simplify complex datasets by reducing their dimensions. It identifies the most important features, or "principal components," that capture the most variance in the data. This helps in visualizing and interpreting data more easily while retaining essential information. PCA works by transforming the original variables into a new set of uncorrelated variables, ordered by the amount of variance they explain. This process allows researchers and analysts to focus on the most significant patterns in the data, making it a valuable tool in fields like data science, machine learning, and image processing.