The Databricks Time Series Library is the optimal choice for managing missing time points and irregular intervals in time-series forecasting. It specializes in time-series analysis, offering features like missing value imputation (through methods such as linear interpolation and forward filling), efficient handling of gaps, and seamless integration with Spark DataFrames. This makes it uniquely suited for projects requiring accurate forecasting despite data irregularities.

• MLlib CrossValidator: Focuses on model performance evaluation, not data imputation.
• Databricks Delta: A storage format enhancing data lake reliability, not designed for missing value handling.
• MLflow Tracking: A tool for experiment logging, lacking specific functionalities for time-series data manipulation.

The Databricks Time Series Library not only addresses the immediate need for handling missing data but also supports comprehensive time-series analysis, including forecasting, anomaly detection, and trend analysis, leveraging distributed processing for scalability.