What process involves adjusting hyperparameters used by an algorithm to improve model performance?

The process of adjusting hyperparameters used by an algorithm to enhance model performance is known as hyperparameter optimization. Hyperparameters are the settings or configurations that are external to the model and cannot be learned from the data itself. Examples include the learning rate, number of trees in a random forest, or the number of layers in a neural network.

Hyperparameter optimization involves systematically searching for the best combination of these settings to maximize the performance of the machine learning model on a validation dataset. This can be done through various methods such as grid search, random search, or more advanced techniques like Bayesian optimization.

In contrast, feature selection involves choosing a subset of relevant features from the dataset to improve model efficiency and performance. Model validation is the process of assessing how well a model performs on unseen data, ensuring that the model's predictions are reliable. Data normalization refers to methods used to scale input variables to a common range, which helps certain algorithms perform better but does not involve adjusting hyperparameters.

Therefore, the correct answer focuses specifically on the critical aspect of tuning hyperparameters to achieve optimal model performance.