Outliers are data points that differ significantly from other observations in a dataset. These points can arise due to variability in the data, measurement errors, or rare events. Outliers can skew statistical analyses and affect the performance of machine learning models by introducing noise and bias. Identifying and handling outliers is an important step in data preprocessing, which can involve techniques like removing, transforming, or capping outliers. Proper management of outliers ensures that the models are trained on data that accurately represents the underlying patterns and relationships.