K-Nearest Neighbors (KNN) is a simple and intuitive machine learning algorithm used for classification and regression tasks. It works by identifying the K closest data points in the training set to a new data point and making predictions based on the majority class (for classification) or the average value (for regression) of those neighbors. The distance between data points is typically calculated using metrics like Euclidean distance. KNN is a non-parametric method, meaning it makes no assumptions about the underlying data distribution. It is easy to implement and can be effective for small datasets, but it may struggle with larger datasets due to its computational cost.