What orchestrates multi-task ML workflows using Databricks jobs?

The correct choice is Databricks Workflows, as it is specifically designed to orchestrate multi-task machine learning workflows within the Databricks environment. Databricks Workflows allows users to easily set up, manage, and execute complex workflows that may involve multiple tasks such as data preparation, feature engineering, model training, and deployment all in sequence or in parallel.

This orchestration capability is vital for managing dependencies among tasks and ensuring that the ML processes run smoothly. It provides robust monitoring and management features that enable the seamless integration of various jobs and tasks, thereby streamlining the machine learning pipeline.

On the other hand, while Databricks ML Models and Databricks Runtime for Machine Learning play critical roles in building and deploying models, they do not specifically handle the orchestration of multi-task workflows. Databricks Dataframes, while a powerful data structure in Spark for working with large datasets, also does not focus on workflow orchestration. Thus, selecting Databricks Workflows as the orchestrator for these multi-task ML workflows aligns correctly with its intended functionality in the Databricks ecosystem.