A. Using a simple 'for' loop to iterate through a predefined set of hyperparameter combinations, training the model for each combination within a Snowpark Python UDF, and storing the results in a Snowflake table.

B. Leveraging a distributed hyperparameter tuning framework like Ray Tune or Dask-ML within Snowpark Python, utilizing Snowflake's compute resources for parallel training and evaluation of different hyperparameter configurations.

C. Employing Scikit-learn's 'GridSearchCV' or 'RandomizedSearchCV' within a single Snowpark Python UDF, processing the entire dataset and hyperparameter search sequentially.

D. Implementing a custom hyperparameter optimization algorithm using Snowpark Python UDFs, manually distributing the hyperparameter search across multiple Snowflake virtual warehouses, and aggregating the results in a central table.

E. Using 'Hyperopt' within a Snowpark Python UDF, relying on its sequential optimization approach without any form of parallelization or distribution of the workload.