A. Cost complexity pruning theory involves selectively trimming decision trees to prevent overfitting and improve generalization. It aims to find the optimal balance between model complexity and predictive accuracy by penalizing overly complex trees through a cost-complexity measure, typically defined by the total number of leaf nodes and a complexity parameter.