Parallelization is not effective for hyperparameter tuning and should be avoided.

Parallelization can significantly speed up hyperparameter tuning by distributing evaluations across multiple resources, but it requires careful configuration and monitoring to ensure efficiency and avoid resource contention.

Parallelization is only used for tuning hyperparameters of distributed models, not single-node models.

Parallelization is identical to sequential tuning but with a different name.