All of the listed strategies are effective in reducing overfitting, each addressing the issue from a different angle. Early Stopping (Option C) prevents overfitting by stopping the training process early. Data Augmentation (Option A) increases the diversity of the training data, making the model more robust. Regularization (Option D) simplifies the model by penalizing large weights, and Dropout (Option B) randomly deactivates neurons to prevent the model from becoming too dependent on any single neuron. Combining these methods often yields the best results in preventing overfitting.