Summary  
This chapter demonstrates how to implement k-fold cross-validation by configuring a parameter grid, setting up an evaluator, and using a CrossValidator to select the best-performing model.  

General domain of usage  
Machine learning model tuning

Master machine learning techniques using PySpark and real estate data. Learn to build, evaluate, and optimize ML pipelines for classification, regression, and clustering tasks using Spark MLlib.

Comprehensive guide to machine learning with PySpark using real estate data, covering classification, regression, clustering, and pipeline optimization.

Cross-Validation in Spark