Principal Component Analysis (PCA) is a dimensionality reduction technique that transforms a large set of variables into a smaller one that still contains most of the information in the original dataset. It achieves this by identifying the directions, called principal components, along which the variation in the data is maximized, allowing for easier visualization and analysis while mitigating noise and redundancy.