In a distributed environment, hyperparameter tuning can be efficiently managed using Spark ML's built-in tools like CrossValidator and TrainValidationSplit. These tools allow for automated tuning by evaluating a model over a grid of parameters or a randomized set of parameters. This approach leverages the distributed computing capabilities of Spark to perform multiple evaluations in parallel, significantly speeding up the tuning process compared to sequential tuning on a single machine.