Principal Component Analysis (PCA) is a widely used statistical technique in data science and machine learning for dimensionality reduction. PCA transforms a dataset with potentially correlated features into a new set of uncorrelated features called principal components. This transformation helps to retain the maximum variance present in the original dataset while reducing its dimensionality, making it easier to visualize and analyze. PCA is particularly useful in preprocessing data for machine learning models, as it can help mitigate issues related to multicollinearity and improve model performance. Additionally, PCA is commonly employed in exploratory data analysis and feature extraction, enabling data scientists to gain insights from high-dimensional datasets.