The parameter gamma in the XGBoost algorithm is a parameter for the cost of complexity of the model, which is used to control overfitting. The larger the gamma value, the more conservative the model is, meaning that it will make fewer splits and reduce the complexity of the model. This will tend to reduce the model's performance, as it will be less capable of accurately predicting unseen data. Conversely, a smaller gamma value will lead to a more complex model, which can result in better performance but is more prone to overfitting.
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