The correct answer is C: Dropout Methods. Dropout is a technique used to prevent overfitting in neural networks by randomly dropping units (along with their connections) from the neural network during training. This helps to ensure that the model generalizes well to new data. Threading (A) and Serialization (B) do not address the issue of model performance on new data. Dimensionality Reduction (D) is more about reducing the number of input features, which doesn't directly address model overfitting when the model already fits training data well.