Parametric models are statistical models that summarize data using a finite set of parameters. These models assume a specific form for the underlying distribution, such as a normal distribution or linear regression. By estimating the parameters from the data, these models can make predictions or infer relationships between variables. One key advantage of parametric models is their simplicity and efficiency, as they require fewer data points to estimate parameters compared to non-parametric models. However, they may not perform well if the assumed distribution does not accurately represent the data, leading to potential biases in the results.