Statistical techniques for drift detection are essential tools in machine learning and data science, used to identify changes in data distributions over time. These techniques help monitor the performance of models by detecting shifts in input data that may lead to degraded predictions. Common methods include the Kolmogorov-Smirnov (KS) test, Population Stability Index (PSI), and Kullback-Leibler divergence, each offering unique insights into the nature and extent of drift. By employing these statistical approaches, practitioners can ensure their models remain robust and reliable in dynamic environments, ultimately enhancing decision-making processes.