The Trials object should be configured to run sequentially without parallelization to ensure stability.

Parallelizing with Trials can be done by setting up a Spark cluster and distributing the trials across nodes, but this may not necessarily improve accuracy.

Increasing the number of trials always leads to higher accuracy, so more trials should always be preferred.

Parallelization is not possible with Hyperopt when using Spark ML models.