Amazon SageMaker Pipelines provides native orchestration of ML workflows with fine-grained control, DAG-based visualization, and seamless integration with SageMaker Studio. AWS documentation explicitly states that Pipelines is designed for end-to-end ML workflow automation and visualization.

SageMaker ML Lineage Tracking records relationships between datasets, models, training jobs, and endpoints, enabling full auditability and governance, which is essential for compliance.

SageMaker Experiments tracks experiment metrics but does not provide lineage-level governance. CodePipeline is a general CI/CD service and lacks ML-specific DAG visualization and lineage tracking.

AWS best practices recommend combining SageMaker Pipelines + SageMaker Studio + ML Lineage Tracking for enterprise-grade ML workflow management.

Therefore, Option C is the correct and AWS-verified solution.