Increasing the number of workers in the Spark cluster (Option B) is the most effective strategy for improving training performance. This approach leverages distributed computing to parallelize tasks, significantly reducing training time for large datasets. While converting the dataset to a Delta table (Option A) can enhance data access speed, it doesn't directly impact the training process. MLflow Tracking (Option C) is valuable for monitoring but doesn't optimize performance. Model parallelism (Option D) requires complex modifications to the model architecture and is more suited to specific scenarios within deep learning frameworks.