text: Title: [BUG] gridsearch with RegressionModel
  Body: I am trying to implement grid search for 3 parameters in the elasticnet regression model from sklearn and wrapping the darts RegressionModel around that. Based on the documentation of [grid search](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.regression_model.html?highlight=regression%20model%20grid%20search#darts.models.forecasting.regression_model.RegressionModel.gridsearch), this is how I initialised the grid search:
  
  ```
  grid_params = {
      'max_iter': [1, 5, 10, 50, 100, 200],
      'alpha': [1e-5, 1e-4, 1e-3, 1e-2, 1e-1, 0.0, 1.0, 10.0, 100.0],
      'l1_ratio': [0.01, 0.1, 0.3, 0.6, 0.9, 1]
  }
  
  sklearn_elasticnet_model = make_pipeline(
      ElasticNet(random_state = 42)
  )
  
  elasticnet_model = RegressionModel(
      lags=target_lags,
      lags_past_covariates=past_cov_lags, 
      lags_future_covariates=future_cov_lags,
      output_chunk_length=forecast_horizon,
      multi_models=True,
      model=sklearn_elasticnet_model
  )
  
  elasticnet_model.gridsearch(grid_params,
                              series=target_series_sample,
                              future_covariates=future_cov_sample,
                              forecast_horizon=24,
                              stride=24)
  ```
  
  However, I am getting this error: `TypeError: RegressionModel.__init__() got an unexpected keyword argument 'max_iter'`. Am I implementing it wrongly?