Which algorithm is commonly used to impute missing values?

The K-nearest neighbors (KNN) algorithm is often used for imputing missing values in datasets due to its simplicity and effectiveness. It works by finding the 'k' closest data points in the feature space and using their values to estimate the missing data. This method is intuitive and can handle both numerical and categorical data. KNN is particularly useful when the dataset has a relatively small number of missing values and is not too large, as it can be computationally expensive.