Principal Component Analysis (PCA) is a technique used in dimensionality reduction to simplify complex datasets while preserving their essential information. The goal of PCA is to reduce the number of variables, or dimensions, in a dataset while retaining as much of the original variance as possible. This is achieved by transforming the original data into a new set of variables, known as principal components, which are linear combinations of the original variables.

The first principal component captures the maximum variance in the data, and each subsequent component captures the remaining variance in a direction orthogonal to the previous components. By focusing on a smaller number of principal components, PCA allows for a more manageable and interpretable dataset, which can enhance the performance of machine learning algorithms and make visualizations more comprehensible.

PCA is particularly useful when dealing with high-dimensional data where many variables may be correlated or redundant. By reducing dimensionality, PCA can help to mitigate the effects of overfitting, improve computational efficiency, and reveal underlying structures in the data that may not be immediately apparent.